Prompt-Based Few-Shot Entity Extraction Pipeline
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Solution Overview
Problem
Existing language models require a large amount of annotated training data to accurately extract new entity types, which is impractical due to the time and resource-intensive nature of generating such data.
Innovation Solution
A prompt-based few-shot entity extraction pipeline system that uses a small set of annotated natural language text documents to train a pre-trained entity extraction model, leveraging sequence generation with explicit attention from prompts to extract new entity types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of annotated training data is used to train a pre-trained language model for new entity types, then extraction accuracy is improved, but training time and resource requirements increase
Solution Approach 1:
The system performs preliminary actions by selecting and preparing relevant training span sequences and prompts before the actual model training. The prompt library is pre-generated with various entity type templates, and during training, only the most relevant pre-prepared sequences are selected and combined with prompts, avoiding the need to process large amounts of raw annotated data from scratch.
Solution Approach 2:
The system extracts only the essential components needed for training by selecting specific training span sequences that are most relevant to the target entity type from the available annotated data. Instead of using all annotated data, the system extracts and combines only the necessary span sequences with corresponding prompts, significantly reducing the effective training data volume while maintaining accuracy.
2Measurement precision
If a large amount of annotated training data is used to train a pre-trained language model for new entity types, then extraction accuracy is improved, but annotation resource requirements increase
Solution Approach 1:
The system extracts only the essential training span sequences needed for effective model training. By selecting a small subset of the most relevant annotated sequences and combining them with structured prompts, the system achieves accurate entity extraction without requiring extensive annotation resources or complex data preparation infrastructure.
Solution Approach 2:
The prompt library serves as an intermediary that bridges the gap between limited annotated data and effective model training. The pre-generated prompts provide structured guidance and contextual information that enhances the value of each annotated example, allowing the system to achieve high extraction accuracy with fewer annotated training instances.
3Productivity
If fewer training data resources are used, then training efficiency is improved, but extraction accuracy deteriorates
Solution Approach 1:
The system changes the parameters of training data representation by combining selected span sequences with structured prompts. This transformation enhances the information density and quality of each training example, allowing the model to achieve high extraction accuracy even with a reduced number of training instances. The prompt structure introduces additional contextual and grammatical information that compensates for the reduced data volume.
Solution Approach 2:
The training data is constructed as a composite by combining selected span sequences with corresponding prompts. This composite structure integrates multiple information sources (the actual entity spans, their contextual sentences, and the structured prompt templates) into a unified training example that provides rich supervisory signals, enabling effective learning from limited data while maintaining high extraction accuracy.
Data Source
AI summary
Techniques are disclosed for prompt-based few-shot entity extraction. The techniques include obtaining an annotated natural language document set for an arbitrary new entity type. A prompt sequence set is generated based on the annotated document set. A pre-trained entity extraction model is trained based on the prompt sequence set to yield a few-shot trained entity extraction model trained to extract at least the arbitrary new entity type. In response to obtaining a test document set, one or more entities of the arbitrary new entity type are extracted from the test document set using the few-shot trained entity extraction model.